Authors
Feifei Qin, Peng Jiang, Xiaolong Chen, Binrui Wang
Published in
ISA transactions. Nov 03, 2026. Epub Nov 03, 2026.
Abstract
Lower-limb rehabilitation exoskeletons driven by pneumatic artificial muscles are challenging to control because pneumatic-muscle hysteresis, force-pressure conversion errors, proportional-valve dynamics, unmodelled pneumatic effects, friction, and operating-condition variations lead to significant nonlinearities and uncertainties. To improve trajectory tracking under these uncertainties, this study proposes an intelligent observer-enhanced adaptive robust controller. First, the dynamic model of the exoskeleton is formulated and decomposed into nominal and uncertain components, and a fuzzy cerebellar model articulation controller neural network is constructed as an intelligent observer to estimate the dominant uncertainties online. Second, the uncertainty estimate is incorporated into a closed-form Udwadia controller framework to satisfy the desired trajectory constraints, while robust and compensation components are designed to attenuate transient tracking mismatches and suppress residual bounded estimation errors. Third, adaptive laws for the observer parameters and uncertainty-bound estimate are developed, and Lyapunov analysis establishes the uniform boundedness and uniform ultimate boundedness of the closed-loop error signals. Finally, comparative simulations and experiments verify that the proposed controller improves trajectory tracking, disturbance rejection, control torque smoothness, and real-time applicability compared with benchmark controllers.
PMID:
43034107
Bibliographic data and abstract were imported from PubMed on 15 Nov 2026.
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